Learning globally-consistent local distance functions for shape-based image retrieval and classification

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Learning globally-consistent local distance functions for shape-based image retrieval and classification (2007)

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by Andrea Frome , Fei Sha , Yoram Singer , Jitendra Malik

Venue:	In ICCV
Citations:	149 - 3 self

BibTeX

@INPROCEEDINGS{Frome07learningglobally-consistent,
    author = {Andrea Frome and Fei Sha and Yoram Singer and Jitendra Malik},
    title = {Learning globally-consistent local distance functions for shape-based image retrieval and classification},
    booktitle = {In ICCV},
    year = {2007}
}

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Abstract

We address the problem of visual category recognition by learning an image-to-image distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patch-based feature vectors common in object recognition work as a basis for our image-to-image distance functions. Our large-margin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions— a different parameterized function for every image of our training set—whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of additional heuristics or training. Here we introduce a different approach that has the advantage that it learns distance functions that are globally consistent in that they can be directly compared for purposes of retrieval and classification. The output of the learning algorithm are weights assigned to the image features, which is intuitively appealing in the computer vision setting: some features are more salient than others, and which are more salient depends on the category, or image, being considered. We train and test using the Caltech 101 object recognition benchmark. Using fifteen training images per category, we achieved a mean recognition rate of 63.2 % and

Keyphrases

shape-based image retrieval globally-consistent local distance function local distance function image-to-image distance function distance function single global distance function object recognition work visual category recognition different approach computer vision setting patch-based feature vector novel approach additional heuristic object recognition benchmark mean recognition rate following property different category large-margin formulation test time different parameterized function fifteen training image image feature distance metric learning learning algorithm

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